code: def _add_anomalies_for_missing_comparisons(
      raw_anomalies: anomalies_pb2.Anomalies,
      config: distribution_validator_pb2.DistributionValidatorConfig,
  ) -> anomalies_pb2.Anomalies:
      """Identifies whether comparison could be done on the configured features.
  
      If comparison was not done for a configured feature, adds an anomaly flagging
      that.
  
      Args:
        raw_anomalies: The Anomalies proto to be checked for comparison.
        config: The config that identifies the features for which distribution
          validation will be done.
  
      Returns:
        An Anomalies proto with anomalies added for features for which comparisons
        could not be done.
      """
      compared_features = set(
          [
              ".".join(info.path.step)
              for info in raw_anomalies.drift_skew_info
          ]
      )
      anomalies = anomalies_pb2.Anomalies()
      anomalies.CopyFrom(raw_anomalies)
      anomalies.anomaly_name_format = (
          anomalies_pb2.Anomalies.AnomalyNameFormat.SERIALIZED_PATH
      )
      for feature in config.default_slice_config.feature:
          if ".".join(feature.path.step) in compared_features:
              continue
          feature_key = ".".join(feature.path.step)
          reason = anomalies.anomaly_info[feature_key].reason.add()
          # TODO(b/239734255): Update with new anomaly type.
          reason.type = (
              anomalies_pb2.AnomalyInfo.Type.STATS_NOT_AVAILABLE
          )
          reason.short_description = "Comparison could not be done."
          reason.description = (
              "Validation could not be done, which could be "
              "due to missing data, use of a comparator that is not suitable for the "
              "feature type, or some other reason."
          )
          anomalies.anomaly_info[feature_key].path.CopyFrom(
              feature.path
          )
          anomalies.anomaly_info[
              feature_key
          ].severity = anomalies_pb2.AnomalyInfo.Severity.ERROR
      return anomalies
  
imports: import os
  from typing import Any, Dict
  from absl import logging
  import tensorflow_data_validation
  from tensorflow_data_validation.utils import path
  from tensorflow_data_validation.utils import schema_util
  from tfx import types
  from tfx.components.statistics_gen import stats_artifact_utils
  from tfx.dsl.components.base import base_executor
  from tfx.orchestration.experimental.core import component_generated_alert_pb2
  from tfx.orchestration.experimental.core import constants
  from tfx.proto import distribution_validator_pb2
  from tfx.proto.orchestration import execution_result_pb2
  from tfx.types import artifact_utils
  from tfx.types import standard_component_specs
  from tfx.utils import json_utils
  from tfx.utils import monitoring_utils
  from tfx.utils import writer_utils
  from google.protobuf import any_pb2
  from tensorflow_metadata.proto.v0 import anomalies_pb2
  from tensorflow_metadata.proto.v0 import schema_pb2
  from tensorflow_metadata.proto.v0 import statistics_pb2
label_desc: Use image node_id 3 for calling a global function with example usage: _add_anomalies_for_missing_comparisons(raw_anomalies, config) and returns: anomalies